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HebbPlot: an intelligent tool for learning and visualizing chromatin mark signatures. | LitMetric

HebbPlot: an intelligent tool for learning and visualizing chromatin mark signatures.

BMC Bioinformatics

Tandy School of Computer Science, University of Tulsa, 800 South Tucker Drive, Tulsa, 74104-9700, OK, USA.

Published: September 2018

AI Article Synopsis

  • Histone modifications are essential for gene regulation and understanding human diseases, but their complexity necessitates computational tools to analyze them effectively.
  • The newly developed software tool, HebbPlot, employs a Hebbian neural network to learn and visualize general chromatin signatures from regions with similar functions, presenting results as interpretable digital images.
  • HebbPlot has been validated through case studies, revealing specific chromatin signatures for active and inactive genes, and different patterns for promoters and enhancers across various tissues.

Article Abstract

Background: Histone modifications play important roles in gene regulation, heredity, imprinting, and many human diseases. The histone code is complex and consists of more than 100 marks. Therefore, biologists need computational tools to characterize general signatures representing the distributions of tens of chromatin marks around thousands of regions.

Results: To this end, we developed a software tool, HebbPlot, which utilizes a Hebbian neural network in learning a general chromatin signature from regions with a common function. Hebbian networks can learn the associations between tens of marks and thousands of regions. HebbPlot presents a signature as a digital image, which can be easily interpreted. Moreover, signatures produced by HebbPlot can be compared quantitatively. We validated HebbPlot in six case studies. The results of these case studies are novel or validating results already reported in the literature, indicating the accuracy of HebbPlot. Our results indicate that promoters have a directional chromatin signature; several marks tend to stretch downstream or upstream. H3K4me3 and H3K79me2 have clear directional distributions around active promoters. In addition, the signatures of high- and low-CpG promoters are different; H3K4me3, H3K9ac, and H3K27ac are the most different marks. When we studied the signatures of enhancers active in eight tissues, we observed that these signatures are similar, but not identical. Further, we identified some histone modifications - H3K36me3, H3K79me1, H3K79me2, and H4K8ac - that are associated with coding regions of active genes. Other marks - H4K12ac, H3K14ac, H3K27me3, and H2AK5ac - were found to be weakly associated with coding regions of inactive genes.

Conclusions: This study resulted in a novel software tool, HebbPlot, for learning and visualizing the chromatin signature of a genetic element. Using HebbPlot, we produced a visual catalog of the signatures of multiple genetic elements in 57 cell types available through the Roadmap Epigenomics Project. Furthermore, we made a progress toward a functional catalog consisting of 22 histone marks. In sum, HebbPlot is applicable to a wide array of studies, facilitating the deciphering of the histone code.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6122555PMC
http://dx.doi.org/10.1186/s12859-018-2312-1DOI Listing

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